# General packages
library(tidyverse)
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library(janitor)
library(plotly)
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library(RColorBrewer)
# Packages for cluster analysis:
library(NbClust)
library(cluster)
library(factoextra)
## Welcome! Related Books: `Practical Guide To Cluster Analysis in R` at https://goo.gl/13EFCZ
library(dendextend)
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## ---------------------
## Welcome to dendextend version 1.9.0
## Type citation('dendextend') for how to cite the package.
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## Type browseVignettes(package = 'dendextend') for the package vignette.
## The github page is: https://github.com/talgalili/dendextend/
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## Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues
## Or contact: <tal.galili@gmail.com>
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## To suppress this message use: suppressPackageStartupMessages(library(dendextend))
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library(ggdendro)
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# Packages for text mining/sentiment analysis/word cloud
library(pdftools)
library(tidytext)
library(wordcloud)
iris_nice <- iris %>%
clean_names() # AAAAAHHHH THIS IS GREAT! I NEED IT FOR OUR SHINY APP!
ggplot(iris_nice) +
geom_point(aes(x = petal_length, y = petal_width, color = species)) # we see that there are species clusters
How many clusters do YOU think should exist, R?
number_est <- NbClust(iris_nice[1:4], min.nc = 2, max.nc = 10, method = "kmeans") # min and max number of clusters, subset first four columns (not including species)
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
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## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
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## *******************************************************************
## * Among all indices:
## * 10 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 2 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 7 as the best number of clusters
## * 1 proposed 8 as the best number of clusters
## * 1 proposed 10 as the best number of clusters
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## ***** Conclusion *****
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## * According to the majority rule, the best number of clusters is 2
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##
## *******************************************************************
# notice that 3 clusters isn't ranked first, 2 is, but we'll probably still go with 3 because that is our conceptual understanding of the number of clusters
Perform k-means clustering with 3 groups:
iris_km <- kmeans(iris_nice[1:4], 3)
iris_km$size # shows number of observations in clusters 1, 2, and 3
## [1] 62 38 50
iris_km$centers # tells us for each variable the multivariate center location in four dimensional space
## sepal_length sepal_width petal_length petal_width
## 1 5.901613 2.748387 4.393548 1.433871
## 2 6.850000 3.073684 5.742105 2.071053
## 3 5.006000 3.428000 1.462000 0.246000
iris_km$cluster
## [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [36] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [71] 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 2 2
## [106] 2 1 2 2 2 2 2 2 1 1 2 2 2 2 1 2 1 2 1 2 2 1 1 2 2 2 2 2 1 2 2 2 2 1 2
## [141] 2 2 1 2 2 2 1 2 2 1
iris_cl <- data.frame(iris_nice, cluster_no = factor(iris_km$cluster))
# Look at a basic ggplot:
ggplot(iris_cl) +
geom_point(aes(x = sepal_length, y = sepal_width, color = cluster_no))
# there is some overlap but don't be alarmed --> we are collapsing multivariate information into two-dimensional space
ggplot(iris_cl) +
geom_point(aes(x = petal_length,
y = petal_width,
color = cluster_no,
pch = species)) +
scale_color_brewer(palette = "Set3")
# notice that there is still overlap - some virginica irises that cluster with the versicolor irises
## make an interactive plot!
plot_ly(x = iris_cl$petal_length,
y = iris_cl$petal_width,
z = iris_cl$sepal_width,
type = "scatter3d",
color = iris_cl$cluster_no, # like in the ggplot
symbol = ~iris_cl$species, # like in the ggplot
marker = list(size = 3),
colors = "Set1")
## No scatter3d mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode